分层分析影像组学模型在肺腺癌诊断中的价值

目的:建立并验证可高效鉴别肺腺癌及其浸润程度的预测模型,并根据结节/肿块性质分层分析模型的预测效能.方法:回顾性分析本院2011年10月-2018年12月经病理证实的肺结节/肿块患者2105例.根据肿瘤性质,分为磨玻璃组(A组,1711例)和实性组(B组,394例),组内以2017年10月为界,分为训练集和测试集.收集...

The value of stratified analysis of radiomics model in the diagnosis of lung adenocarcinoma

Objective:To establish and verify an efficiently model to distinguish lung adenocarcinoma and its invasiveness,and further identify the predictive performance of the model according to the types of nodule/mass.Methods:In the study,2105 patients with pathologically confirmed lung nodules/masses were retrospectively enrolled from October 2011 to December 2018.According to the characteristics of the tumor,they were divided into ground glass group(group A-1711 cases)and solid group(group B-394 cases).With the boundary of October 2017,patients in each group were divided into the training set and test set.Image features and demographic data were collected.3D slicer was used to manually delineate region of interest(ROI).Pyraodiomics was used to extract radiomic features,and max-relevance and min-redundancy(mRMR)and least absolute shrinkage and selection operator(LASSO)algorithms were used to filter features,reduce dimensionality.The prediction models were then constructed,and the area under the curve(AUC)value of the receiver operator characteristic curve,the calibration curve and the goodness of fit test,and the clinical decision curve were used to respectively evaluate the prediction accuracy,calibration,and clinical applicability of the model.Results:Independent radiomics model,traditional model and fusion radiomics model were established in group A and B respectively.In group A:the AUC values of fusion radiomics model in training set,test set,and external validation set were 0.92(0.90~0.93),0.94(0.93~0.96),0.87(0.82~0.91).In group B:The AUC values of the fusion radiomics model in the training set,test set and external validation set were 0.85(0.80~0.90),0.80(0.72~0.89),0.70(0.54~0.85).The De-long test showed a statistical difference in the comparison of the efficacy between the test set radiomics models and the traditional model in group A(P<0.05).The efficacy results between the radiomics models and the traditional model in group B were not statistically different.In both group A and group B,the Hosmer-Lemeshow goodness-of-fit test fusion radiomics model,traditional model P-value was greater than 0.05,and the predicted values of fusion radiomics model in the calibration curve overlapped with the standard baseline.The decision curve had the highest net profitability for the fusion radiomics model.The results were partially validated in the external validation set.Conclusion:Radiomics can improve the diagnostic efficiency of lung adenocarcinoma compared with traditional diagnostic models.Furthermore,fusion radiomics model demonstrated a better performance in ground-glass lung nodules/mases with a better prediction accuracy,calibration,and clinical applicability,compared with solid lung nodules/masses.